{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp learner"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learner"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">fastai Learner extensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "import pickle\n",
    "import warnings\n",
    "from fastai.callback.core import CancelBatchException\n",
    "from fastai.learner import Learner, load_learner, Recorder\n",
    "from fastai.callback.schedule import *\n",
    "from fastai.optimizer import Adam\n",
    "from fastai.losses import MSELossFlat\n",
    "from tsai.imports import *\n",
    "from tsai.models.utils import *\n",
    "from tsai.models.InceptionTimePlus import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@patch\n",
    "def show_batch(self:Learner, **kwargs):\n",
    "    self.dls.show_batch(**kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@patch\n",
    "def remove_all_cbs(self:Learner, max_iters=10):\n",
    "    i = 0\n",
    "    while len(self.cbs) > 0 and i < max_iters:  \n",
    "        self.remove_cbs(self.cbs)\n",
    "        i += 1\n",
    "    if len(self.cbs) > 0: print(f'Learner still has {len(self.cbs)} callbacks: {self.cbs}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@patch\n",
    "def one_batch(self:Learner, i, b): \n",
    "    # Fixes a bug that will be managed in the next release of fastai\n",
    "    self.iter = i\n",
    "#     b_on_device = tuple( e.to(device=self.dls.device) for e in b if hasattr(e, \"to\")) if self.dls.device is not None else b\n",
    "    b_on_device = to_device(b, device=self.dls.device) if self.dls.device is not None else b\n",
    "    self._split(b_on_device)\n",
    "    self._with_events(self._do_one_batch, 'batch', CancelBatchException)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@patch\n",
    "def transform(self:Learner, df:pd.DataFrame):\n",
    "    \"Applies sklearn-type pipeline transforms\"\n",
    "    \n",
    "    if self.pipelines is None: return df\n",
    "    for pipeline in self.pipelines:\n",
    "        df = pipeline.transform(df)\n",
    "    return df\n",
    "\n",
    "\n",
    "@patch\n",
    "def inverse_transform(self:Learner, df:pd.DataFrame):\n",
    "    \"Applies sklearn-type pipeline inverse transforms\"\n",
    "    if self.pipelines is None: return df\n",
    "    for pipeline in self.pipelines:\n",
    "        df = pipeline.inverse_transform(df)\n",
    "    return df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "⚠️ Important: save_all and load_all methods are designed for small datasets only. If you are using a larger dataset, you should use the standard save and load_learner methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@patch\n",
    "def save_all(self:Learner, path='export', dls_fname='dls', model_fname='model', learner_fname='learner', verbose=False):\n",
    "    path = Path(path)\n",
    "    if not os.path.exists(path): os.makedirs(path)\n",
    "\n",
    "    self.dls_type = self.dls.__class__.__name__\n",
    "    if self.dls_type == \"MixedDataLoaders\":\n",
    "        self.n_loaders = (len(self.dls.loaders), len(self.dls.loaders[0].loaders))\n",
    "        dls_fnames = []\n",
    "        for i,dl in enumerate(self.dls.loaders):\n",
    "            for j,l in enumerate(dl.loaders):\n",
    "                l = l.new(num_workers=1)\n",
    "                torch.save(l, path/f'{dls_fname}_{i}_{j}.pth')\n",
    "                dls_fnames.append(f'{dls_fname}_{i}_{j}.pth')\n",
    "    else:\n",
    "        dls_fnames = []\n",
    "        self.n_loaders = len(self.dls.loaders)\n",
    "        for i,dl in enumerate(self.dls):\n",
    "            dl = dl.new(num_workers=1)\n",
    "            torch.save(dl, path/f'{dls_fname}_{i}.pth')\n",
    "            dls_fnames.append(f'{dls_fname}_{i}.pth')\n",
    "\n",
    "    # Saves the model along with optimizer\n",
    "    self.model_dir = path\n",
    "    self.save(f'{model_fname}', with_opt=True)\n",
    "\n",
    "    # Export learn without the items and the optimizer state for inference\n",
    "    self.export(path/f'{learner_fname}.pkl')\n",
    "    \n",
    "    pv(f'Learner saved:', verbose)\n",
    "    pv(f\"path          = '{path}'\", verbose)\n",
    "    pv(f\"dls_fname     = '{dls_fnames}'\", verbose)\n",
    "    pv(f\"model_fname   = '{model_fname}.pth'\", verbose)\n",
    "    pv(f\"learner_fname = '{learner_fname}.pkl'\", verbose)\n",
    "    \n",
    "    \n",
    "def load_all(path='export', dls_fname='dls', model_fname='model', learner_fname='learner', device=None, pickle_module=pickle, verbose=False):\n",
    "\n",
    "    if isinstance(device, int): device = torch.device('cuda', device)\n",
    "    elif device is None: device = default_device()\n",
    "    if device.type == 'cpu': cpu = True\n",
    "    else: cpu = None\n",
    "\n",
    "    path = Path(path)\n",
    "    learn = load_learner(path/f'{learner_fname}.pkl', cpu=cpu, pickle_module=pickle_module)\n",
    "    learn.path = path\n",
    "    learn.model_dir = Path() # '.'\n",
    "    learn.load(f'{model_fname}', with_opt=True, device=device)\n",
    "\n",
    "    \n",
    "    if learn.dls_type == \"MixedDataLoaders\":\n",
    "        from tsai.data.mixed import MixedDataLoader, MixedDataLoaders\n",
    "        dls_fnames = []\n",
    "        _dls = []\n",
    "        for i in range(learn.n_loaders[0]):\n",
    "            _dl = []\n",
    "            for j in range(learn.n_loaders[1]):\n",
    "                l = torch.load(path/f'{dls_fname}_{i}_{j}.pth', map_location=device, pickle_module=pickle_module)\n",
    "                l = l.new(num_workers=0)\n",
    "                l.to(device)\n",
    "                dls_fnames.append(f'{dls_fname}_{i}_{j}.pth')\n",
    "                _dl.append(l)\n",
    "            _dls.append(MixedDataLoader(*_dl, path=learn.dls.path, device=device, shuffle=l.shuffle))\n",
    "        learn.dls = MixedDataLoaders(*_dls, path=learn.dls.path, device=device)\n",
    "\n",
    "    else:\n",
    "        loaders = []\n",
    "        dls_fnames = []\n",
    "        for i in range(learn.n_loaders):\n",
    "            dl = torch.load(path/f'{dls_fname}_{i}.pth', map_location=device, pickle_module=pickle_module)\n",
    "            dl = dl.new(num_workers=0)\n",
    "            dl.to(device)\n",
    "            first(dl)\n",
    "            loaders.append(dl)\n",
    "            dls_fnames.append(f'{dls_fname}_{i}.pth')\n",
    "        learn.dls = type(learn.dls)(*loaders, path=learn.dls.path, device=device)\n",
    "\n",
    "\n",
    "    pv(f'Learner loaded:', verbose)\n",
    "    pv(f\"path          = '{path}'\", verbose)\n",
    "    pv(f\"dls_fname     = '{dls_fnames}'\", verbose)\n",
    "    pv(f\"model_fname   = '{model_fname}.pth'\", verbose)\n",
    "    pv(f\"learner_fname = '{learner_fname}.pkl'\", verbose)\n",
    "    return learn\n",
    "\n",
    "load_learner_all = load_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tsai.data.core import get_ts_dls\n",
    "from tsai.utils import remove_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learner saved:\n",
      "path          = '/Users/nacho/tmp'\n",
      "dls_fname     = '['dls_0.pth', 'dls_1.pth']'\n",
      "model_fname   = 'model.pth'\n",
      "learner_fname = 'learner.pkl'\n",
      "Learner loaded:\n",
      "path          = '/Users/nacho/tmp'\n",
      "dls_fname     = '['dls_0.pth', 'dls_1.pth']'\n",
      "model_fname   = 'model.pth'\n",
      "learner_fname = 'learner.pkl'\n",
      "/Users/nacho/tmp directory removed.\n"
     ]
    }
   ],
   "source": [
    "X = np.random.rand(100, 2, 10)\n",
    "dls = get_ts_dls(X)\n",
    "learn = Learner(dls, InceptionTimePlus(2, 1), loss_func=MSELossFlat())\n",
    "learn.save_all(Path.home()/'tmp', verbose=True)\n",
    "learn2 = load_all(Path.home()/'tmp', verbose=True)\n",
    "remove_dir(Path.home()/'tmp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@patch\n",
    "@delegates(subplots)\n",
    "def plot_metrics(self: Recorder, nrows=None, ncols=None, figsize=None, final_losses=True, perc=.5, **kwargs):\n",
    "    \n",
    "    n_values = len(self.recorder.values)\n",
    "    if n_values < 2:\n",
    "        print('not enough values to plot a chart')\n",
    "        return\n",
    "    \n",
    "    # Prepare results dataframe\n",
    "    train_metrics = self.train_metrics\n",
    "    valid_metrics = self.valid_metrics\n",
    "    metrics = np.stack(self.values)\n",
    "    names = self.metric_names[1:-1]\n",
    "    if not train_metrics and not valid_metrics:\n",
    "        names = [n for n in names if \"loss\" in n]\n",
    "    elif not train_metrics:\n",
    "        names = [f\"valid_{n}\" if (not 'valid' in n and not 'loss' in n) else n for n in names]\n",
    "    elif not valid_metrics:\n",
    "        names = [f\"train_{n}\" if (not 'train' in n and not 'loss' in n) else n for n in names]\n",
    "    results = pd.DataFrame(metrics, columns=names)\n",
    "    \n",
    "    # Final losses\n",
    "    if final_losses:\n",
    "        sel_idxs = round(n_values * perc)\n",
    "        if sel_idxs < 2:\n",
    "            final_losses = False\n",
    "        else:\n",
    "            results['train_final_loss'] = results['train_loss']\n",
    "            if valid_metrics: \n",
    "                results['valid_final_loss'] = results['valid_loss']\n",
    "\n",
    "    # set of metrics names\n",
    "    names = results.columns\n",
    "    metric_names = list(dict.fromkeys([n.replace('train_', '').replace('valid_', '') for n in results.columns]))\n",
    "     \n",
    "    # Plot\n",
    "    n = len(metric_names)\n",
    "    if nrows is None and ncols is None:\n",
    "        if n <= 3: \n",
    "            nrows = 1\n",
    "        else:\n",
    "            nrows = int(math.sqrt(n))\n",
    "        ncols = int(np.ceil(n / nrows))\n",
    "    elif nrows is None: nrows = int(np.ceil(n / ncols))\n",
    "    elif ncols is None: ncols = int(np.ceil(n / nrows))\n",
    "    figsize = figsize or (ncols * 6 + ncols - 1, nrows * 4 + nrows - 1)\n",
    "    fig, axs = subplots(nrows, ncols, figsize=figsize, **kwargs)\n",
    "    axs = axs.flatten()[:n]\n",
    "\n",
    "    for name in names:\n",
    "        xs = np.arange(0, len(results))\n",
    "        if name in ['train_loss', 'valid_loss']: \n",
    "            ax_idx = 0\n",
    "            m = results[name].values\n",
    "            title = 'losses'\n",
    "        elif name in ['train_final_loss', 'valid_final_loss']: \n",
    "            ax_idx = 1\n",
    "            m = results.loc[len(results) - sel_idxs:, name].values\n",
    "            xs = xs[-sel_idxs:]\n",
    "            title = 'final losses'\n",
    "        else: \n",
    "            ax_idx = metric_names.index(name.replace(\"valid_\", \"\").replace(\"train_\", \"\")) + final_losses\n",
    "            m = results[name].values\n",
    "            title = name.replace(\"valid_\", \"\").replace(\"train_\", \"\")\n",
    "        if 'train' in name:\n",
    "            color = '#1f77b4'\n",
    "            label = 'train'\n",
    "        else:\n",
    "            color = '#ff7f0e'\n",
    "            label = 'valid' if (m != [None] * len(m)).all() else None\n",
    "            axs[ax_idx].grid(color='gainsboro', linewidth=.5)\n",
    "        axs[ax_idx].plot(xs, m, color=color, label=label)\n",
    "        axs[ax_idx].set_xlim(xs[0], xs[-1])\n",
    "        axs[ax_idx].legend(loc='best')\n",
    "        axs[ax_idx].set_title(title)\n",
    "        axs[ax_idx].grid(color='gainsboro', linewidth=.5)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "@patch\n",
    "@delegates(subplots)\n",
    "def plot_metrics(self: Learner, **kwargs):\n",
    "    self.recorder.plot_metrics(**kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "all_arch_names =  ['FCN', 'FCNPlus', 'InceptionTime', 'InceptionTimePlus', 'InCoordTime', 'XCoordTime', 'InceptionTimePlus17x17', 'InceptionTimePlus32x32', \n",
    "                   'InceptionTimePlus47x47', 'InceptionTimePlus62x62', 'InceptionTimeXLPlus', 'MultiInceptionTimePlus', 'MiniRocketClassifier', \"MiniRocket\",\n",
    "                   'MiniRocketRegressor', 'MiniRocketVotingClassifier', 'MiniRocketVotingRegressor', 'MiniRocket', 'MiniRocketPlus', \n",
    "                   'MultiRocket', 'MultiRocketPlus', 'InceptionRocketPlus', 'MLP', 'gMLP', 'MultiInputNet', 'OmniScaleCNN', 'RNN', 'LSTM', 'GRU', \n",
    "                   'RNNPlus', 'LSTMPlus', 'GRUPlus', 'RNN_FCN', 'LSTM_FCN', 'GRU_FCN', 'MRNN_FCN', 'MLSTM_FCN', 'MGRU_FCN', \n",
    "                   'RNN_FCNPlus', 'LSTM_FCNPlus', 'GRU_FCNPlus', 'MRNN_FCNPlus', 'MLSTM_FCNPlus', 'MGRU_FCNPlus', 'ROCKET', 'RocketClassifier', \n",
    "                   'RocketRegressor', 'ResCNN', 'ResNet', 'ResNetPlus', 'TCN', 'TSPerceiver', 'TST', 'TSTPlus', 'MultiTSTPlus', 'TSiT', 'TSiTPlus', \n",
    "                   'TabFusionTransformer', 'TSTabFusionTransformer', 'TabModel', 'TabTransformer', 'GatedTabTransformer', 'TransformerModel', 'XCM', 'XCMPlus', \n",
    "                   'xresnet1d18', 'xresnet1d34', 'xresnet1d50', 'xresnet1d101', 'xresnet1d152', 'xresnet1d18_deep', 'xresnet1d34_deep', 'xresnet1d50_deep', \n",
    "                   'xresnet1d18_deeper', 'xresnet1d34_deeper', 'xresnet1d50_deeper', 'XResNet1dPlus', 'xresnet1d18plus', 'xresnet1d34plus', \n",
    "                   'xresnet1d50plus', 'xresnet1d101plus', 'xresnet1d152plus', 'xresnet1d18_deepplus', 'xresnet1d34_deepplus', 'xresnet1d50_deepplus', \n",
    "                   'xresnet1d18_deeperplus', 'xresnet1d34_deeperplus', 'xresnet1d50_deeperplus', 'XceptionTime', 'XceptionTimePlus', 'mWDN', 'mWDNPlus',\n",
    "                   'TSSequencer', 'TSSequencerPlus', \"PatchTST\", \"ConvTran\", \"ConvTranPlus\",\n",
    "                   \"RNNAttention\", \"LSTMAttention\", \"GRUAttention\", \"RNNAttentionPlus\", \"LSTMAttentionPlus\", \"GRUAttentionPlus\", \n",
    "                   \"TransformerRNNPlus\", \"TransformerLSTMPlus\", \"TransformerGRUPlus\", \"Hydra\", \"HydraPlus\", \"HydraMultiRocket\", \"HydraMultiRocketPlus\"]\n",
    "\n",
    "\n",
    "def get_arch(arch_name):\n",
    "    if not isinstance(arch_name, str): return arch_name\n",
    "    arch = None\n",
    "    if arch_name == \"FCN\":  \n",
    "        from tsai.models.FCN import FCN\n",
    "        arch = FCN\n",
    "    elif arch_name == \"FCNPlus\":  \n",
    "        from tsai.models.FCNPlus import FCNPlus\n",
    "        arch = FCNPlus\n",
    "    elif arch_name == \"InceptionTime\":  \n",
    "        from tsai.models.InceptionTime import InceptionTime\n",
    "        arch = InceptionTime\n",
    "    elif arch_name == \"InceptionTimePlus\":  \n",
    "        from tsai.models.InceptionTimePlus import InceptionTimePlus\n",
    "        arch = InceptionTimePlus\n",
    "    elif arch_name == \"InCoordTime\":  \n",
    "        from tsai.models.InceptionTimePlus import InCoordTime\n",
    "        arch = InCoordTime\n",
    "    elif arch_name == \"XCoordTime\":  \n",
    "        from tsai.models.InceptionTimePlus import XCoordTime\n",
    "        arch = XCoordTime\n",
    "    elif arch_name == \"InceptionTimePlus17x17\":  \n",
    "        from tsai.models.InceptionTimePlus import InceptionTimePlus17x17\n",
    "        arch = InceptionTimePlus17x17\n",
    "    elif arch_name == \"InceptionTimePlus32x32\":  \n",
    "        from tsai.models.InceptionTimePlus import InceptionTimePlus32x32\n",
    "        arch = InceptionTimePlus32x32\n",
    "    elif arch_name == \"InceptionTimePlus47x47\":  \n",
    "        from tsai.models.InceptionTimePlus import InceptionTimePlus47x47\n",
    "        arch = InceptionTimePlus47x47\n",
    "    elif arch_name == \"InceptionTimePlus62x62\":  \n",
    "        from tsai.models.InceptionTimePlus import InceptionTimePlus62x62\n",
    "        arch = InceptionTimePlus62x62\n",
    "    elif arch_name == \"InceptionTimeXLPlus\":  \n",
    "        from tsai.models.InceptionTimePlus import InceptionTimeXLPlus\n",
    "        arch = InceptionTimeXLPlus\n",
    "    elif arch_name == \"MultiInceptionTimePlus\":  \n",
    "        from tsai.models.InceptionTimePlus import MultiInceptionTimePlus\n",
    "        arch = MultiInceptionTimePlus\n",
    "    elif arch_name == \"MiniRocketClassifier\":  \n",
    "        from tsai.models.MINIROCKET import MiniRocketClassifier\n",
    "        arch = MiniRocketClassifier\n",
    "    elif arch_name == \"MiniRocketRegressor\":  \n",
    "        from tsai.models.MINIROCKET import MiniRocketRegressor\n",
    "        arch = MiniRocketRegressor\n",
    "    elif arch_name == \"MiniRocketVotingClassifier\":  \n",
    "        from tsai.models.MINIROCKET import MiniRocketVotingClassifier\n",
    "        arch = MiniRocketVotingClassifier\n",
    "    elif arch_name == \"MiniRocketVotingRegressor\":  \n",
    "        from tsai.models.MINIROCKET import MiniRocketVotingRegressor\n",
    "        arch = MiniRocketVotingRegressor\n",
    "    elif arch_name == \"MiniRocketFeaturesPlus\":  \n",
    "        from tsai.models.MINIROCKETPlus_Pytorch import MiniRocketFeaturesPlus\n",
    "        arch = MiniRocketFeaturesPlus\n",
    "    elif arch_name == \"MiniRocket\":  \n",
    "        from tsai.models.MINIROCKET_Pytorch import MiniRocket\n",
    "        arch = MiniRocket\n",
    "    elif arch_name == \"MultiRocket\":  \n",
    "        from tsai.models.MultiRocketPlus import MultiRocket\n",
    "        arch = MultiRocket\n",
    "    elif arch_name == \"MultiRocketPlus\":  \n",
    "        from tsai.models.MultiRocketPlus import MultiRocketPlus\n",
    "        arch = MultiRocketPlus\n",
    "    elif arch_name == \"MiniRocketPlus\":  \n",
    "        from tsai.models.MINIROCKETPlus_Pytorch import MiniRocketPlus\n",
    "        arch = MiniRocketPlus\n",
    "    elif arch_name == \"MiniRocketHead\":  \n",
    "        from tsai.models.MINIROCKETPlus_Pytorch import MiniRocketHead\n",
    "        arch = MiniRocketHead\n",
    "    elif arch_name == \"InceptionRocketFeaturesPlus\":  \n",
    "        from tsai.models.MINIROCKETPlus_Pytorch import InceptionRocketFeaturesPlus\n",
    "        arch = InceptionRocketFeaturesPlus\n",
    "    elif arch_name == \"InceptionRocketPlus\":  \n",
    "        from tsai.models.MINIROCKETPlus_Pytorch import InceptionRocketPlus\n",
    "        arch = InceptionRocketPlus\n",
    "    elif arch_name == \"Hydra\":\n",
    "        from tsai.models.HydraPlus import Hydra\n",
    "        arch = Hydra\n",
    "    elif arch_name == \"HydraPlus\":\n",
    "        from tsai.models.HydraPlus import HydraPlus\n",
    "        arch = HydraPlus\n",
    "    elif arch_name == \"HydraMultiRocket\":\n",
    "        from tsai.models.HydraMultiRocketPlus import HydraMultiRocket\n",
    "        arch = HydraMultiRocket\n",
    "    elif arch_name == \"HydraMultiRocketPlus\":\n",
    "        from tsai.models.HydraMultiRocketPlus import HydraMultiRocketPlus\n",
    "        arch = HydraMultiRocketPlus\n",
    "    elif arch_name == \"MLP\":  \n",
    "        from tsai.models.MLP import MLP\n",
    "        arch = MLP\n",
    "    elif arch_name == \"gMLP\":  \n",
    "        from tsai.models.gMLP import gMLP\n",
    "        arch = gMLP\n",
    "    elif arch_name == \"MultiInputNet\":  \n",
    "        from tsai.models.MultiInputNet import MultiInputNet\n",
    "        arch = MultiInputNet\n",
    "    elif arch_name == \"OmniScaleCNN\":  \n",
    "        from tsai.models.OmniScaleCNN import OmniScaleCNN\n",
    "        arch = OmniScaleCNN\n",
    "    elif arch_name == \"RNN\":  \n",
    "        from tsai.models.RNN import RNN\n",
    "        arch = RNN\n",
    "    elif arch_name == \"LSTM\":  \n",
    "        from tsai.models.RNN import LSTM\n",
    "        arch = LSTM\n",
    "    elif arch_name == \"GRU\":  \n",
    "        from tsai.models.RNN import GRU\n",
    "        arch = GRU\n",
    "    elif arch_name == \"RNNPlus\":  \n",
    "        from tsai.models.RNNPlus import RNNPlus\n",
    "        arch = RNNPlus\n",
    "    elif arch_name == \"LSTMPlus\":  \n",
    "        from tsai.models.RNNPlus import LSTMPlus\n",
    "        arch = LSTMPlus\n",
    "    elif arch_name == \"GRUPlus\":  \n",
    "        from tsai.models.RNNPlus import GRUPlus\n",
    "        arch = GRUPlus\n",
    "    elif arch_name == \"RNN_FCN\":  \n",
    "        from tsai.models.RNN_FCN import RNN_FCN\n",
    "        arch = RNN_FCN\n",
    "    elif arch_name == \"LSTM_FCN\":  \n",
    "        from tsai.models.RNN_FCN import LSTM_FCN\n",
    "        arch = LSTM_FCN\n",
    "    elif arch_name == \"GRU_FCN\":  \n",
    "        from tsai.models.RNN_FCN import GRU_FCN\n",
    "        arch = GRU_FCN\n",
    "    elif arch_name == \"MRNN_FCN\":  \n",
    "        from tsai.models.RNN_FCN import MRNN_FCN\n",
    "        arch = MRNN_FCN\n",
    "    elif arch_name == \"MLSTM_FCN\":  \n",
    "        from tsai.models.RNN_FCN import MLSTM_FCN\n",
    "        arch = MLSTM_FCN\n",
    "    elif arch_name == \"MGRU_FCN\":  \n",
    "        from tsai.models.RNN_FCN import MGRU_FCN\n",
    "        arch = MGRU_FCN\n",
    "    elif arch_name == \"RNN_FCNPlus\":  \n",
    "        from tsai.models.RNN_FCNPlus import RNN_FCNPlus\n",
    "        arch = RNN_FCNPlus\n",
    "    elif arch_name == \"LSTM_FCNPlus\":  \n",
    "        from tsai.models.RNN_FCNPlus import LSTM_FCNPlus\n",
    "        arch = LSTM_FCNPlus\n",
    "    elif arch_name == \"GRU_FCNPlus\":  \n",
    "        from tsai.models.RNN_FCNPlus import GRU_FCNPlus\n",
    "        arch = GRU_FCNPlus\n",
    "    elif arch_name == \"MRNN_FCNPlus\":  \n",
    "        from tsai.models.RNN_FCNPlus import MRNN_FCNPlus\n",
    "        arch = MRNN_FCNPlus\n",
    "    elif arch_name == \"MLSTM_FCNPlus\":  \n",
    "        from tsai.models.RNN_FCNPlus import MLSTM_FCNPlus\n",
    "        arch = MLSTM_FCNPlus\n",
    "    elif arch_name == \"MGRU_FCNPlus\":  \n",
    "        from tsai.models.RNN_FCNPlus import MGRU_FCNPlus\n",
    "        arch = MGRU_FCNPlus\n",
    "    elif arch_name == \"PatchTST\":  \n",
    "        from tsai.models.PatchTST import PatchTST\n",
    "        arch = PatchTST\n",
    "    elif arch_name == \"ROCKET\":  \n",
    "        from tsai.models.ROCKET_Pytorch import ROCKET\n",
    "        arch = ROCKET\n",
    "    elif arch_name == \"RocketClassifier\":  \n",
    "        from tsai.models.ROCKET import RocketClassifier\n",
    "        arch = RocketClassifier\n",
    "    elif arch_name == \"RocketRegressor\":  \n",
    "        from tsai.models.ROCKET import RocketRegressor\n",
    "        arch = RocketRegressor\n",
    "    elif arch_name == \"ResCNN\":  \n",
    "        from tsai.models.ResCNN import ResCNN\n",
    "        arch = ResCNN\n",
    "    elif arch_name == \"ResNet\":  \n",
    "        from tsai.models.ResNet import ResNet\n",
    "        arch = ResNet\n",
    "    elif arch_name == \"ResNetPlus\":  \n",
    "        from tsai.models.ResNetPlus import ResNetPlus\n",
    "        arch = ResNetPlus\n",
    "    elif arch_name == \"TCN\":  \n",
    "        from tsai.models.TCN import TCN\n",
    "        arch = TCN\n",
    "    elif arch_name == \"TSPerceiver\":  \n",
    "        from tsai.models.TSPerceiver import TSPerceiver\n",
    "        arch = TSPerceiver\n",
    "    elif arch_name == \"TST\":  \n",
    "        from tsai.models.TST import TST\n",
    "        arch = TST\n",
    "    elif arch_name == \"TSTPlus\":  \n",
    "        from tsai.models.TSTPlus import TSTPlus\n",
    "        arch = TSTPlus\n",
    "    elif arch_name == \"MultiTSTPlus\":  \n",
    "        from tsai.models.TSTPlus import MultiTSTPlus\n",
    "        arch = MultiTSTPlus\n",
    "    elif arch_name == \"TSiT\":  \n",
    "        from tsai.models.TSiTPlus import TSiT\n",
    "        arch = TSiT\n",
    "    elif arch_name == \"TSiTPlus\":  \n",
    "        from tsai.models.TSiTPlus import TSiTPlus\n",
    "        arch = TSiTPlus\n",
    "    elif arch_name == \"TSSequencer\":  \n",
    "        from tsai.models.TSSequencerPlus import TSSequencer\n",
    "        arch = TSSequencer\n",
    "    elif arch_name == \"TSSequencerPlus\":  \n",
    "        from tsai.models.TSSequencerPlus import TSSequencerPlus\n",
    "        arch = TSSequencerPlus\n",
    "    elif arch_name == \"TabFusionTransformer\":  \n",
    "        from tsai.models.TabFusionTransformer import TabFusionTransformer\n",
    "        arch = TabFusionTransformer\n",
    "    elif arch_name == \"TSTabFusionTransformer\":  \n",
    "        from tsai.models.TabFusionTransformer import TSTabFusionTransformer\n",
    "        arch = TSTabFusionTransformer\n",
    "    elif arch_name == \"TabModel\":  \n",
    "        from tsai.models.TabModel import TabModel\n",
    "        arch = TabModel\n",
    "    elif arch_name == \"TabTransformer\":  \n",
    "        from tsai.models.TabTransformer import TabTransformer\n",
    "        arch = TabTransformer\n",
    "    elif arch_name == \"GatedTabTransformer\":  \n",
    "        from tsai.models.GatedTabTransformer import GatedTabTransformer\n",
    "        arch = GatedTabTransformer\n",
    "    elif arch_name == \"TransformerModel\":  \n",
    "        from tsai.models.TransformerModel import TransformerModel\n",
    "        arch = TransformerModel\n",
    "    elif arch_name == \"XCM\":  \n",
    "        from tsai.models.XCM import XCM\n",
    "        arch = XCM\n",
    "    elif arch_name == \"XCMPlus\":  \n",
    "        from tsai.models.XCMPlus import XCMPlus\n",
    "        arch = XCMPlus\n",
    "    elif arch_name == \"xresnet1d18\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d18\n",
    "        arch = xresnet1d18\n",
    "    elif arch_name == \"xresnet1d34\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d34\n",
    "        arch = xresnet1d34\n",
    "    elif arch_name == \"xresnet1d50\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d50\n",
    "        arch = xresnet1d50\n",
    "    elif arch_name == \"xresnet1d101\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d101\n",
    "        arch = xresnet1d101\n",
    "    elif arch_name == \"xresnet1d152\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d152\n",
    "        arch = xresnet1d152\n",
    "    elif arch_name == \"xresnet1d18_deep\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d18_deep\n",
    "        arch = xresnet1d18_deep\n",
    "    elif arch_name == \"xresnet1d34_deep\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d34_deep\n",
    "        arch = xresnet1d34_deep\n",
    "    elif arch_name == \"xresnet1d50_deep\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d50_deep\n",
    "        arch = xresnet1d50_deep\n",
    "    elif arch_name == \"xresnet1d18_deeper\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d18_deeper\n",
    "        arch = xresnet1d18_deeper\n",
    "    elif arch_name == \"xresnet1d34_deeper\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d34_deeper\n",
    "        arch = xresnet1d34_deeper\n",
    "    elif arch_name == \"xresnet1d50_deeper\":  \n",
    "        from tsai.models.XResNet1d import xresnet1d50_deeper\n",
    "        arch = xresnet1d50_deeper\n",
    "    elif arch_name == \"XResNet1dPlus\":  \n",
    "        from tsai.models.XResNet1dPlus import XResNet1dPlus\n",
    "        arch = XResNet1dPlus\n",
    "    elif arch_name == \"xresnet1d18plus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d18plus\n",
    "        arch = xresnet1d18plus\n",
    "    elif arch_name == \"xresnet1d34plus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d34plus\n",
    "        arch = xresnet1d34plus\n",
    "    elif arch_name == \"xresnet1d50plus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d50plus\n",
    "        arch = xresnet1d50plus\n",
    "    elif arch_name == \"xresnet1d101plus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d101plus\n",
    "        arch = xresnet1d101plus\n",
    "    elif arch_name == \"xresnet1d152plus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d152plus\n",
    "        arch = xresnet1d152plus\n",
    "    elif arch_name == \"xresnet1d18_deepplus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d18_deepplus\n",
    "        arch = xresnet1d18_deepplus\n",
    "    elif arch_name == \"xresnet1d34_deepplus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d34_deepplus\n",
    "        arch = xresnet1d34_deepplus\n",
    "    elif arch_name == \"xresnet1d50_deepplus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d50_deepplus\n",
    "        arch = xresnet1d50_deepplus\n",
    "    elif arch_name == \"xresnet1d18_deeperplus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d18_deeperplus\n",
    "        arch = xresnet1d18_deeperplus\n",
    "    elif arch_name == \"xresnet1d34_deeperplus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d34_deeperplus\n",
    "        arch = xresnet1d34_deeperplus\n",
    "    elif arch_name == \"xresnet1d50_deeperplus\":  \n",
    "        from tsai.models.XResNet1dPlus import xresnet1d50_deeperplus\n",
    "        arch = xresnet1d50_deeperplus\n",
    "    elif arch_name == \"XceptionTime\":  \n",
    "        from tsai.models.XceptionTime import XceptionTime\n",
    "        arch = XceptionTime\n",
    "    elif arch_name == \"XceptionTimePlus\":  \n",
    "        from tsai.models.XceptionTimePlus import XceptionTimePlus\n",
    "        arch = XceptionTimePlus\n",
    "    elif arch_name == \"mWDN\":  \n",
    "        from tsai.models.mWDN import mWDN\n",
    "        arch = mWDN\n",
    "    elif arch_name == \"mWDNPlus\":  \n",
    "        from tsai.models.mWDN import mWDNPlus\n",
    "        arch = mWDNPlus\n",
    "    elif arch_name == \"RNNAttention\":  \n",
    "        from tsai.models.RNNAttention import RNNAttention\n",
    "        arch = RNNAttention\n",
    "    elif arch_name == \"LSTMAttention\":  \n",
    "        from tsai.models.RNNAttention import LSTMAttention\n",
    "        arch = LSTMAttention\n",
    "    elif arch_name == \"GRUAttention\":  \n",
    "        from tsai.models.RNNAttention import GRUAttention\n",
    "        arch = GRUAttention\n",
    "    elif arch_name == \"RNNAttentionPlus\":  \n",
    "        from tsai.models.RNNAttentionPlus import RNNAttentionPlus\n",
    "        arch = RNNAttentionPlus\n",
    "    elif arch_name == \"LSTMAttentionPlus\":  \n",
    "        from tsai.models.RNNAttentionPlus import LSTMAttentionPlus\n",
    "        arch = LSTMAttentionPlus\n",
    "    elif arch_name == \"GRUAttentionPlus\":  \n",
    "        from tsai.models.RNNAttentionPlus import GRUAttentionPlus\n",
    "        arch = GRUAttentionPlus\n",
    "    elif arch_name == \"TransformerRNNPlus\":\n",
    "        from tsai.models.TransformerRNNPlus import TransformerRNNPlus\n",
    "        arch = TransformerRNNPlus\n",
    "    elif arch_name == \"TransformerLSTMPlus\":\n",
    "        from tsai.models.TransformerRNNPlus import TransformerLSTMPlus\n",
    "        arch = TransformerLSTMPlus\n",
    "    elif arch_name == \"TransformerGRUPlus\":\n",
    "        from tsai.models.TransformerRNNPlus import TransformerGRUPlus\n",
    "        arch = TransformerGRUPlus\n",
    "    elif arch_name in [\"ConvTran\", \"ConvTranPlus\"]: \n",
    "        from tsai.models.ConvTranPlus import ConvTranPlus\n",
    "        arch = ConvTranPlus\n",
    "    else: \n",
    "        raise ValueError(f\"Architecture {arch_name} not found. Please, check the name is correct.\")\n",
    "    \n",
    "    assert arch.__name__.replace(\"Plus\", \"\") == arch_name.replace(\"Plus\", \"\"), f\"{arch.__name__} - {arch_name}\"\n",
    "    return arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for arch_name in all_arch_names:\n",
    "    get_arch(arch_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@delegates(build_ts_model)\n",
    "def ts_learner(dls, arch=None, c_in=None, c_out=None, seq_len=None, d=None, \n",
    "               s_cat_idxs=None, s_cat_embeddings=None, s_cat_embedding_dims=None, s_cont_idxs=None, \n",
    "               o_cat_idxs=None, o_cat_embeddings=None, o_cat_embedding_dims=None, o_cont_idxs=None,\n",
    "               splitter=trainable_params, loss_func=None, opt_func=Adam, lr=defaults.lr, cbs=None, metrics=None, path=None,\n",
    "               model_dir='models', wd=None, wd_bn_bias=False, train_bn=True, moms=(0.95,0.85,0.95), \n",
    "               train_metrics=False, valid_metrics=True, seed=None, **kwargs)->Learner:\n",
    "\n",
    "    if seed is not None:\n",
    "        set_seed(seed, reproducible=True)\n",
    "    \n",
    "    if isinstance(arch, nn.Module): \n",
    "        model = arch\n",
    "        if kwargs: \n",
    "            warnings.warn(\"You have passed kwargs to a model that is already intantiated. They will not have any effect.\", UserWarning)\n",
    "    else:\n",
    "        if arch is None: arch = InceptionTimePlus\n",
    "        elif isinstance(arch, str): arch = get_arch(arch)\n",
    "        model = build_ts_model(arch, dls=dls, c_in=c_in, c_out=c_out, seq_len=seq_len, d=d, \n",
    "                               s_cat_idxs=s_cat_idxs, s_cat_embeddings=s_cat_embeddings, s_cat_embedding_dims=s_cat_embedding_dims, s_cont_idxs=s_cont_idxs, \n",
    "                               o_cat_idxs=o_cat_idxs, o_cat_embeddings=o_cat_embeddings, o_cat_embedding_dims=o_cat_embedding_dims, o_cont_idxs=o_cont_idxs, **kwargs)\n",
    "    if hasattr(model, \"backbone\") and hasattr(model, \"head\"):\n",
    "        splitter = ts_splitter\n",
    "    if loss_func is None:\n",
    "        if hasattr(dls, 'loss_func'): loss_func = dls.loss_func\n",
    "        elif hasattr(dls, 'train_ds') and hasattr(dls.train_ds, 'loss_func'): loss_func = dls.train_ds.loss_func\n",
    "        elif hasattr(dls, 'cat') and not dls.cat: loss_func = MSELossFlat()\n",
    "        \n",
    "    learn = Learner(dls=dls, model=model,\n",
    "                    loss_func=loss_func, opt_func=opt_func, lr=lr, cbs=cbs, metrics=metrics, path=path, splitter=splitter,\n",
    "                    model_dir=model_dir, wd=wd, wd_bn_bias=wd_bn_bias, train_bn=train_bn, moms=moms, )\n",
    "\n",
    "    if hasattr(learn, \"recorder\"):\n",
    "            learn.recorder.train_metrics = train_metrics\n",
    "            learn.recorder.valid_metrics = valid_metrics if len(dls.valid) > 0 else False\n",
    "    \n",
    "    # keep track of args for loggers\n",
    "    store_attr('arch', self=learn)\n",
    "\n",
    "    return learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@delegates(build_tsimage_model)\n",
    "def tsimage_learner(dls, arch=None, pretrained=False,\n",
    "               loss_func=None, opt_func=Adam, lr=defaults.lr, cbs=None, metrics=None, path=None,\n",
    "               model_dir='models', wd=None, wd_bn_bias=False, train_bn=True, moms=(0.95,0.85,0.95),\n",
    "               **kwargs):\n",
    "\n",
    "    if arch is None: \n",
    "        from fastai.vision.models.xresnet import xresnet34\n",
    "        arch = xresnet34\n",
    "    elif isinstance(arch, str): arch = get_arch(arch)\n",
    "    model = build_tsimage_model(arch, dls=dls, pretrained=pretrained, **kwargs)\n",
    "    learn = Learner(dls=dls, model=model,\n",
    "                    loss_func=loss_func, opt_func=opt_func, lr=lr, cbs=cbs, metrics=metrics, path=path,\n",
    "                    model_dir=model_dir, wd=wd, wd_bn_bias=wd_bn_bias, train_bn=train_bn, moms=moms)\n",
    "\n",
    "    # keep track of args for loggers\n",
    "    store_attr('arch', self=learn)\n",
    "\n",
    "    return learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "@patch\n",
    "def decoder(self:Learner, o): return L([self.dls.decodes(oi) for oi in o])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tsai.data.core import *\n",
    "from tsai.data.external import get_UCR_data\n",
    "from tsai.models.FCNPlus import FCNPlus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: OliveOil\n",
      "X      : (60, 1, 570)\n",
      "y      : (60,)\n",
      "splits : (#30) [0,1,2,3,4,5,6,7,8,9...] (#30) [30,31,32,33,34,35,36,37,38,39...] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "X, y, splits = get_UCR_data('OliveOil', verbose=True, split_data=False)\n",
    "tfms  = [None, [TSCategorize()]]\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms)\n",
    "learn = ts_learner(dls, FCNPlus)\n",
    "for p in learn.model.parameters():\n",
    "    p.requires_grad=False\n",
    "test_eq(count_parameters(learn.model), 0)\n",
    "learn.freeze()\n",
    "test_eq(count_parameters(learn.model), 1540)\n",
    "learn.unfreeze()\n",
    "test_eq(count_parameters(learn.model), 264580)\n",
    "\n",
    "learn = ts_learner(dls, 'FCNPlus')\n",
    "for p in learn.model.parameters():\n",
    "    p.requires_grad=False\n",
    "test_eq(count_parameters(learn.model), 0)\n",
    "learn.freeze()\n",
    "test_eq(count_parameters(learn.model), 1540)\n",
    "learn.unfreeze()\n",
    "test_eq(count_parameters(learn.model), 264580)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.show_batch();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.metrics import accuracy\n",
    "from tsai.data.preprocessing import TSRobustScale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.480875</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>1.390461</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.476655</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>1.387370</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y, splits = get_UCR_data('OliveOil', split_data=False)\n",
    "tfms  = [None, TSClassification()]\n",
    "batch_tfms = TSRobustScale()\n",
    "dls = get_ts_dls(X, y, tfms=tfms, splits=splits, batch_tfms=batch_tfms)\n",
    "learn = ts_learner(dls, FCNPlus, metrics=accuracy, train_metrics=True)\n",
    "learn.fit_one_cycle(2)\n",
    "learn.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"./models\"): os.mkdir(\"./models\")\n",
    "if not os.path.exists(\"./data\"): os.mkdir(\"./data\")\n",
    "np.save(\"data/X_test.npy\", X[splits[1]])\n",
    "np.save(\"data/y_test.npy\", y[splits[1]])\n",
    "learn.export(\"./models/test.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "IPython.notebook.save_checkpoint();",
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/nacho/notebooks/tsai/nbs/018_learner.ipynb saved at 2023-07-04 14:15:02\n",
      "Correct notebook to script conversion! 😃\n",
      "Tuesday 04/07/23 14:15:05 CEST\n"
     ]
    },
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       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#|eval: false\n",
    "#|hide\n",
    "from tsai.export import get_nb_name; nb_name = get_nb_name(locals())\n",
    "from tsai.imports import create_scripts; create_scripts(nb_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
